4f2ea4913c
* squash Signed-off-by: Jason <jasoli@nvidia.com> * add comments Signed-off-by: Jason <jasoli@nvidia.com> * style and cleanup Signed-off-by: Jason <jasoli@nvidia.com> * cleanup Signed-off-by: Jason <jasoli@nvidia.com> * add new test file Signed-off-by: Jason <jasoli@nvidia.com> * syntax Signed-off-by: Jason <jasoli@nvidia.com> * style Signed-off-by: Jason <jasoli@nvidia.com> * typo Signed-off-by: Jason <jasoli@nvidia.com> * update Signed-off-by: Jason <jasoli@nvidia.com> * update Signed-off-by: Jason <jasoli@nvidia.com> * update Signed-off-by: Jason <jasoli@nvidia.com> * try again Signed-off-by: Jason <jasoli@nvidia.com> * wip Signed-off-by: Jason <jasoli@nvidia.com> * style; ci should fail Signed-off-by: Jason <jasoli@nvidia.com> * final Signed-off-by: Jason <jasoli@nvidia.com>
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytest
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import torch
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from nemo.collections.tts.torch.data import CharMelAudioDataset
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class TestCharDataset:
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@pytest.mark.run_only_on('CPU')
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@pytest.mark.unit
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@pytest.mark.torch_tts
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def test_dataset(self, test_data_dir):
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manifest_path = os.path.join(test_data_dir, 'tts/mini_ljspeech/manifest.json')
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sup_path = os.path.join(test_data_dir, 'tts/mini_ljspeech/sup')
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dataset = CharMelAudioDataset(
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manifest_filepath=manifest_path, sample_rate=22050, supplementary_folder=sup_path
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)
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dataloader = torch.utils.data.DataLoader(dataset, 2, collate_fn=dataset._collate_fn)
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data, _, _, _, _, _, _ = next(iter(dataloader))
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class TestPhoneDataset:
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@pytest.mark.run_only_on('CPU')
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@pytest.mark.unit
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@pytest.mark.torch_tts
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def test_dataset(self, test_data_dir):
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manifest_path = os.path.join(test_data_dir, 'tts/mini_ljspeech/manifest.json')
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sup_path = os.path.join(test_data_dir, 'tts/mini_ljspeech/sup')
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dataset = CharMelAudioDataset(
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manifest_filepath=manifest_path, sample_rate=22050, supplementary_folder=sup_path
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)
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dataloader = torch.utils.data.DataLoader(dataset, 2, collate_fn=dataset._collate_fn)
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_, _, _, _, _, _, _ = next(iter(dataloader))
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